Aurel_AI: Automating an Institutional Help Desk Using an LLM Chatbot

The Aurel_AI research project focuses on creating a virtual help desk for universities, delivering accurate information about academic programs, regulations, processes, and personnel to both internal and external clients. Traditional call centers often grapple with outdated data, limited knowledge, and high staff turnover, leading to inaccurate responses and long wait times. Generative AI models, particularly Large Language Models (LLMs), offer a promising solution for automated help desks. These models can comprehend poorly structured queries and generate appropriate answers. However, they may encounter “hallucinations” due to insufficient training data. Ensuring accurate and comprehensive information involves specific data collection, validation, and updating methodologies. Techniques like Fine-Tuning and Retrieval-Augmented Generation (RAG) are essential for specific use cases. While both methods have pros and cons, balancing cost-effective infrastructure is crucial for a precise, flexible, and user-friendly system.